The carrier-phase (CP) technique based on the Global Navigation Satellite System (GNSS) has proved to be a useful spatial tool for remote and precise time transfer. In order to improve the robustness and stability of the time transfer solution for a time link, a new CP approach based on a combination of GPS, BeiDou (BDS), and Galileo satellite systems is proposed in this study. The mathematical model for the obtained unique time transfer solution is discussed. Three GNSS stations that can track GPS, BeiDou, and Galileo satellites were used, and two time links are established to assess the performance of the approach. Multi-GNSS time transfer outperforms single GNSS by increasing the number of available satellites and improving the time dilution of precision. For the long time link, with a geodetic distance of 7537.5 km, the RMS value of the combined multi-system solution improves by 18.8%, 59.4%, and 35.0% compared to GPS-only, BDS-only, and Galileo-only, respectively. The average frequency stability improves by 12.9%, 62.3%, and 36.0%, respectively. For the short time link, with a geodetic distance of 4.7 m, the improvement after combining the three GNSSs is 6.7% for GPS-only, 52.6% for BDS-only, and 38.2% for Galileo-only.

On 20 July 2021, parts of China’s Henan Province received the highest precipitation levels ever recorded in the region. Floods caused by heavy rainfall resulted in hundreds of casualties and tens of billions of dollars’ worth of property loss. Due to the highly dynamic nature of flood disasters, rapid and timely spatial monitoring is conducive for early disaster prevention, mid-term disaster relief, and post-disaster reconstruction. However, existing remote sensing satellites cannot provide high-resolution flood monitoring results. Seeing as spaceborne global navigation satellite system-reflectometry (GNSS-R) can observe the Earth’s surface with high temporal and spatial resolutions, it is expected to provide a new solution to the problem of flood hazards. Here, using the Cyclone Global Navigation Satellite System (CYGNSS) L1 data, we first counted various signal-to-noise ratios and the corresponding reflectivity to surface features in Henan Province. Subsequently, we analyzed changes in the delay-Doppler map of CYGNSS when the observed area was submerged and not submerged. Finally, we determined the submerged area affected by extreme precipitation using the threshold detection method. The results demonstrated that the flood range retrieved by CYGNSS agreed with that retrieved by the Soil Moisture Active Passive (SMAP) mission and the precipitation data retrieved and measured by the Global Precipitation Measurement mission and meteorological stations. Compared with the SMAP results, those obtained by CYGNSS have a higher spatial resolution and can monitor changes in the areas affected by the floods over a shorter period.

This study proposes two models for precise time transfer using the BeiDou Navigation Satellite System triple-frequency signals: ionosphere-free (IF) combined precise point positioning (PPP) model with two dual-frequency combinations (IF-PPP1) and ionosphere-free combined PPP model with a single triple-frequency combination (IF-PPP2). A dataset with a short baseline (with a common external time frequency) and a long baseline are used for performance assessments. The results show that IF-PPP1 and IF-PPP2 models can both be used for precise time transfer using BeiDou Navigation Satellite System (BDS) triple-frequency signals, and the accuracy and stability of time transfer is the same in both cases, except for a constant system bias caused by the hardware delay of different frequencies, which can be removed by the parameter estimation and prediction with long time datasets or by a priori calibration.

The Global Navigation Satellite System (GNSS) is currently one of the important tools for landslide monitoring and early warning. However, the majority of GNSS devices are installed in mountainous areas and a variety of vegetation. These harsh environments lead to defective signals at high elevation angles, rendering real-time successive and reliable positioning results for monitoring difficult. In this study, an environmental model derived from signal-to-noise ratio (SNR) is proposed to enhance the precision and convergence time of positioning in harsh environments. A series of experiments are conducted on weighting and ambiguity-fixed models to evaluate performance. The results indicate that the proposed SNR-dependent environment model could lead to a significant improvement in precision and convergence time; with an obtained root mean squared result on the millimeter level, a convergence time of a few seconds, and utilization which could reach 100%, for continuous and reliable positioning results. These results indicate that the proposed SNR-dependent environment model enhances the performance of GNSS monitoring and early warning to provide continuous and reliable positioning results in real-time.

Time-differenced carrier phase velocity estimations (TDCPVE) and Doppler velocity estimations (DVE) are two commonly used methods for precise velocity estimation with a stand-alone GPS receiver. As TDCPVE require a minimum of four satellites for parameter estimation, the time-differenced velocity estimation (TDVE) model was developed by combining the TDCPVE model and time-differenced pseudo-range velocity estimation model. A limitation of the TDVE is that it can show reduced solution availability, particularly during harsh conditions. DVE model theory is stricter than that of the TDVE model, which yields more reliability when vehicle maneuverability is strong. However, its accuracy is low due to the low precision of Doppler observations. Nevertheless, the advantages and disadvantages of the TDVE and DVE are complementary. This study presents a TD–DVE method by combining the two models. This method can achieve improved performance by fully using all useful information contained in the GPS and Doppler observations. Static and dynamic data were used to verify and analyze the performance of the TD–DVE. The results show that the accuracy of the TD–DVE was improved compared with the DVE. Compared with the TDVE method, TD–DVE has a much higher solution rate at poor observation conditions and improved accuracy during changing vehicle dynamic conditions. Overall, the TD–DVE model enhances the robustness of the individual velocity estimation methods and improves usability.

The carrier-phase (CP) technique, based on the global navigation satellite system (GNSS), has proved to be a useful spatial tool for remote and precise time transfer. In this study, we analysed the performance of CP time and frequency transfer with different GNSSs, using a satellite orbit and clock products collected from three analysis centres (ACs): CODE, WHU, and GFZ. From the time and frequency transfer experiments with six international time links, we concluded that the CP technique, based on different GNSSs, can all achieve a sub-nanosecond level accuracy for time and frequency transfer. Statistical analysis of the experiments shows that the global positioning system (GPS) solution can obtain the mean root mean square (RMS) of the six time links: 0.321 ns for cod (satellite products of CODE), 0.347 ns for whn (satellite products of WHU), and 0.288 ns for gbm (satellite products of GFZ). For Russian global navigation satellite system (GLONASS) solutions, the mean RMS is 0.306 ns for cod, 0.371 ns for whn, and 0.298 ns for gbm. In terms of emerging GNSSs, the Galileo can achieve a mean RMS of 0.293 ns for cod, 0.356 ns for whn, 0.295 ns for gbm, and the BeiDou navigation satellite system (BDS) can achieve a mean RMS of 0.294 ns for cod, 0.333 ns for whn, and 0.303 ns for gbm. The mean RMS is 0.288 ns for GPS, 0.298 ns for GLONASS, 0.295 ns for Galileo, and 0.303 ns for BDS. Regarding the three ACs, cod and gbm solutions show better performance than whn solutions. With respect to the frequency stability of CP technique by different GNSSs, Galileo and BDS are comparable to GPS at different averaging time intervals, while GLONASS is significantly worse than GPS.